Miguel R. D. Rodrigues

research

∙ 09/12/2023

Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding

Solving linear inverse problems plays a crucial role in numerous applica...

0 Shaik Basheeruddin Shah, et al. ∙

research

∙ 04/19/2023

Generalization and Estimation Error Bounds for Model-based Neural Networks

Model-based neural networks provide unparalleled performance for various...

0 Avner Shultzman, et al. ∙

research

∙ 10/18/2022

Information-theoretic Characterizations of Generalization Error for the Gibbs Algorithm

Various approaches have been developed to upper bound the generalization...

0 Gholamali Aminian, et al. ∙

research

∙ 10/02/2022

Learning Algorithm Generalization Error Bounds via Auxiliary Distributions

Generalization error boundaries are essential for comprehending how well...

15 Gholamali Aminian, et al. ∙

research

∙ 06/29/2022

Theoretical Perspectives on Deep Learning Methods in Inverse Problems

In recent years, there have been significant advances in the use of deep...

0 Jonathan Scarlett, et al. ∙

research

∙ 02/24/2022

An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift

A common assumption in semi-supervised learning is that the labeled, unl...

7 Gholamali Aminian, et al. ∙

research

∙ 10/20/2021

Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover

In this paper, we consider deep neural networks for solving inverse prob...

0 Wei Pu, et al. ∙

research

∙ 07/28/2021

Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information

Bounding the generalization error of a supervised learning algorithm is ...

15 Gholamali Aminian, et al. ∙

research

∙ 02/03/2021

Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms

Generalization error bounds are critical to understanding the performanc...

15 Gholamali Aminian, et al. ∙

research

∙ 10/23/2020

Jensen-Shannon Information Based Characterization of the Generalization Error of Learning Algorithms

Generalization error bounds are critical to understanding the performanc...

49 Gholamali Aminian, et al. ∙

research

∙ 09/16/2020

Image Separation with Side Information: A Connected Auto-Encoders Based Approach

X-radiography (X-ray imaging) is a widely used imaging technique in art ...

11 Wei Pu, et al. ∙

research

∙ 06/18/2020

Model-Aware Regularization For Learning Approaches To Inverse Problems

There are various inverse problems – including reconstruction problems a...

7 Jaweria Amjad, et al. ∙

research

∙ 04/02/2019

Lautum Regularization for Semi-supervised Transfer Learning

Transfer learning is a very important tool in deep learning as it allows...

24 Daniel Jakubovitz, et al. ∙

research

∙ 01/31/2019

Deep Learning for Inverse Problems: Bounds and Regularizers

Inverse problems arise in a number of domains such as medical imaging, r...

16 Jaweria Amjad, et al. ∙

research

∙ 08/03/2018

Generalization Error in Deep Learning

Deep learning models have lately shown great performance in various fiel...

0 Daniel Jakubovitz, et al. ∙

research

∙ 07/22/2018

Hardware-Limited Task-Based Quantization

Quantization plays a critical role in digital signal processing systems....

0 Nir Shlezinger, et al. ∙

research

∙ 06/26/2018

Coupled Dictionary Learning for Multi-contrast MRI Reconstruction

Medical imaging tasks often involve multiple contrasts, such as T1- and ...

2 Pingfan Song, et al. ∙

research

∙ 06/26/2018

Multimodal Image Denoising based on Coupled Dictionary Learning

In this paper, we propose a new multimodal image denoising approach to a...

6 Pingfan Song, et al. ∙

research

∙ 06/26/2018

Multi-modal Image Processing based on Coupled Dictionary Learning

In real-world scenarios, many data processing problems often involve het...

2 Pingfan Song, et al. ∙

research

∙ 09/25/2017

Multimodal Image Super-Resolution via Joint Sparse Representations induced by Coupled Dictionaries

Real-world data processing problems often involve various image modaliti...

0 Pingfan Song, et al. ∙

research

∙ 05/23/2017

Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems

Security, privacy, and fairness have become critical in the era of data ...

0 Jure Sokolic, et al. ∙

research

∙ 10/14/2016

Generalization Error of Invariant Classifiers

This paper studies the generalization error of invariant classifiers. In...

0 Jure Sokolic, et al. ∙

research

∙ 07/14/2016

Multi-modal dictionary learning for image separation with application in art investigation

In support of art investigation, we propose a new source separation meth...

0 Nikos Deligiannis, et al. ∙

research

∙ 07/11/2016

Bounds on the Number of Measurements for Reliable Compressive Classification

This paper studies the classification of high-dimensional Gaussian signa...

0 Hugo Reboredo, et al. ∙

research

∙ 05/26/2016

Robust Large Margin Deep Neural Networks

The generalization error of deep neural networks via their classificatio...

0 Jure Sokolic, et al. ∙

research

∙ 05/20/2016

X-ray image separation via coupled dictionary learning

In support of art investigation, we propose a new source sepa- ration me...

0 Nikos Deligiannis, et al. ∙

research

∙ 08/07/2015

Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification

This paper considers the classification of linear subspaces with mismatc...

0 Jure Sokolic, et al. ∙

research

∙ 03/11/2015

Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction

We propose and analyze an online algorithm for reconstructing a sequence...

0 João F. C. Mota, et al. ∙

research

∙ 12/01/2014

Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information

This paper offers a characterization of fundamental limits on the classi...

0 Francesco Renna, et al. ∙

research

∙ 10/10/2014

Compressed Sensing With Side Information: Geometrical Interpretation and Performance Bounds

We address the problem of Compressed Sensing (CS) with side information....

0 João F. C. Mota, et al. ∙

Miguel R. D. Rodrigues

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